package ds_industry_2025.ds.ds02.T2

import org.apache.hudi.DataSourceWriteOptions.{PARTITIONPATH_FIELD, PRECOMBINE_FIELD, RECORDKEY_FIELD}
import org.apache.hudi.QuickstartUtils.getQuickstartWriteConfigs
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.{SparkSession, functions}
import org.apache.spark.sql.functions.{col, desc, lit, row_number, to_timestamp}

import java.text.SimpleDateFormat
import java.util.Calendar
/*
    3、抽取ods_ds_hudi库base_province表中昨天的分区（子任务一生成的分区）数据，并结合dim_province最新分区现有的数据，根据id合
    并数据到dwd_ds_hudi库中dim_province的分区表（合并是指对dwd层数据进行插入或修改，需修改的数据以id为合并字段，根据
    create_time排序取最新的一条），分区字段为etl_date且值与ods_ds_hudi库的相对应表该值相等，并添加dwd_insert_user、
    dwd_insert_time、dwd_modify_user、dwd_modify_time四列,其中dwd_insert_user、dwd_modify_user均填写“user1”。若该
    条数据第一次进入数仓dwd层则dwd_insert_time、dwd_modify_time均填写当前操作时间，并进行数据类型转换。若该数据在进入dwd层时
    发生了合并修改，则dwd_insert_time时间不变，dwd_modify_time存当前操作时间，其余列存最新的值。id作为primaryKey，
    dwd_modify_time作为preCombineField。使用spark-shell在表dwd.dim_province最新分区中，查询该分区中数据的条数，将结果截
    图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下；
 */
object t3 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t3")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .enableHiveSupport()
      .getOrCreate()

    val day=Calendar.getInstance()
    val current_time=new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(day.getTime)
    day.add(Calendar.DATE,-1)
    val yesterday=new SimpleDateFormat("yyyyMMdd").format(day.getTime)

    val ods_path="hdfs://192.168.40.110:9000/user/hive/warehouse/ods_ds_hudi.db/base_province"
    val dwd_path="hdfs://192.168.40.110:9000/user/hive/warehouse/dwd_ds_hudi.db/dim_province"

    val ods = spark.read.format("hudi").load(ods_path)
      .where(col("etl_date") === yesterday)
      .drop("etl_date")
      .withColumn("dwd_insert_user", lit("user1"))
      .withColumn("dwd_insert_time", to_timestamp(lit(current_time)))
      .withColumn("dwd_modify_user", lit("user1"))
      .withColumn("dwd_modify_time", to_timestamp(lit(current_time)))

    spark.read.format("hudi").load(dwd_path).createOrReplaceTempView("dwd")
    val dwd = spark.read.format("hudi").load(dwd_path)
      .where("etl_date=(select max(etl_date) from dwd)")
      .withColumn("dwd_modify_time", to_timestamp(lit(current_time)))
      .drop("etl_date")


    ods.unionAll(dwd)
      .withColumn(
        "dwd_insert_time",
        functions.min("dwd_insert_time") over(Window.partitionBy("id"))
      )
      .withColumn(
        "row",
        row_number()  over(Window.partitionBy("id").orderBy(desc("create_time")))
      )
      .filter(col("row")===1)
      .drop("row")
      .withColumn("etl_date",lit(yesterday))
      .write.format("hudi").mode("append")
      .options(getQuickstartWriteConfigs)
      .option(RECORDKEY_FIELD.key(),"id")
      .option(PRECOMBINE_FIELD.key(),"dwd_modify_time")
      .option(PARTITIONPATH_FIELD.key(),"etl_date")
      .option("hoodie.table.name","dim_province")
      .save(dwd_path)





    spark.close()
  }

}
